Elucidation of respiratory microbial communities and the feasibility of breath diagnostics based on microbial volatile organic compounds (VOCs) in adult cystic fibrosis patients

Abstract

This thesis aimed to contribute to a more profound understanding of the microbial communities present in the airways of adult cystic fibrosis (CF) patients. Additionally, a breath analytical method was developed to detect microbial biomarkers (volatile organic compounds; VOCs) in exhaled air of the infected host and the feasibility of the method was tested in a CF outpatient clinic. Only culture-independent methods, such as single-strand conformation polymorphism (SSCP) fingerprinting and Illumina sequencing, revealed the broad spectrum of microorganisms associated with sputum from CF patients. Profiling of bacterial communities was performed and revealed no strong correlation between lung function and individual relative abundances of single species. In a polymicrobial perspective, however, distinct and persistent subgroups of bacterial communities could be defined by the individual compositions of species. In contrast to bacteria, an unexpected high richness of fungi was detected. Likewise, high fluctuation rates in species numbers were observed over time and between different patients, suggesting rather low colonization abilities of fungi in CF airways. For the breath analytical diagnostic approach, it was at first demonstrated that VOCs released by microorganism allowed discrimination of pathogens in vitro. The individual VOCs compositions monitored in exhaled breath of infected patients in vivo allowed distinction between CF patients and controls. Overall, this thesis supports the hypothesis of polymicrobial consortia being involved in pulmonary infections and provides for the first time an overview of the entire microbiome in a broader CF cohort, including fungi and bacteria. Furthermore, the potential of exhaled VOCs analysis to identify patients with certain pulmonary infections was demonstrated which may facilitate rapid and highly accurate diagnosis in the future.